Multi Objective for PMU Placement in Compressed Distribution Network Considering Cost and Accuracy of State Estimation

Abstract

A phasor measurement unit (PMU) can provide phasor measurements to the distribution network to improve observability. Based on pre-configuration and existing measurements, a network compression method is proposed to reduce PMU candidate locations. Taking the minimum number of PMUs and the lowest state estimation error as the objective functions and taking full observability of distribution network as the constraint, a multi objective model of optimal PMU placement (OPP) is proposed. A hybrid state estimator based on supervisory control and data acquisition (SCADA) and PMU measurements is proposed. To reduce the number of PMUs required for full observability, SCADA measurement data are also considered into the constraint by update and equivalent. In addition, a non-dominated sorting genetic algorithm-II (NSGA-II) is applied to solve the model to get the Pareto set. Finally, the optimal solution is selected from the Pareto set by the technique for order preference by similarity to ideal solution (TOPSIS). The effectiveness of the proposed method is verified by IEEE standard bus systems.

Distribution system state estimation with stability assessment using bio-inspired computing

“…The set of solutions for each test systems will vary with same number of PMUs, but with different coverage values. Table 2 displays the optimum locations of PMU which are identified as 2, 4, 9, 11 and 13 with the highest coverage value of 20, which is compared to the greedy algorithm [13] and NSGA [15]. It is observed that the proposed method achieved best locations with minimum PMU.…”

A Probabilistic Multi-Objective Model for Phasor Measurement Units Placement in the Presence of Line Outage

“…By analyzing the data in Table 3, it can be seen that the difference of the placement schemes between the two models lies in the locations of the PMUs. For example, 29 PMUs are needed in both models under case 2, and the majority of PMUs are placed at the same location, namely, bus 1, 3,9,12,20,22,24,27,29,30,32,33,35,39,47,51,53,55,57; meanwhile, the rest few PMUs are placed at a different location, namely, bus 4,6,11,15,19,36,41,44,46,49 in multi-objective model and bus 5,7,14,18,38,40,42,43,45,50 in single-objective model. Obviously, the sum of adjacent buses of bus 4,6,11,15,19,36,41,44,46,49 is larger than bus 5,7,14,18,38,40,42,43,45,50. Thus, compared to single-objective models, parts of PMUs in multi-objective models are placed at the buses with more adjacent buses.…”

“…Apparently, the developed placement model can provide solutions with higher reliability. ,11,12,15,19,20,22,24,26,28,29,30,31, 32,33,35,36,37,38,41,45,46,47,50,51,53,54,56,57 Multi-objective 29 0.00180 1,3,4,6,9,11,12,15,19,20,22,24,27,29,30,32,33,35,36,39,41,44,46,47,49,51,53,55,57 Single-objective 29 0.00298 1,3,5,7,9,12,14,18,20,22,24,27,29,30,32,33,35,38,39,40,42,43,45,47,…”

“…In addition, to obtain the final solution from the Pareto front quickly and accurately, the fuzzy satisfying approach was applied in [16,17] and sparse neighborhood surroundings in [18]. Furthermore, the accuracy of state estimation was taken into consideration, subject to the constraint of supervisory control and data acquisition (SCADA) measurement data in [19] and the uncertainties of power system conditions in [20].Literatures aforementioned analyzed the system observability from a deterministic view, including two contrary states, i.e., observable or unobservable. However, it was investigated in a probabilistic manner in [21][22][23][24].…”

“…In addition, to obtain the final solution from the Pareto front quickly and accurately, the fuzzy satisfying approach was applied in [16,17] and sparse neighborhood surroundings in [18]. Furthermore, the accuracy of state estimation was taken into consideration, subject to the constraint of supervisory control and data acquisition (SCADA) measurement data in [19] and the uncertainties of power system conditions in [20].…”